OpenRaiser/Intern-Atlas huggingface dataset:
Cybersecurity for Autonomous Vehicles
Venue: arXiv cs.CR 2025
Year: 2025

The increasing adoption of autonomous vehicles is bringing a major shift in the automotive industry. However, as these vehicles become more connected, cybersecurity threats have emerged as a serious concern. Protecting the security and integrity of autonomous systems is essential to prevent malicious activities that can harm passengers, other road users, and the overall transportation network. This paper focuses on addressing the cybersecurity issues in autonomous vehicles by examining the challenges and risks involved, which are important for building a secure future. Since autonomous vehicles depend on the communication between sensors, artificial intelligence, external infrastructure, and other systems, they are exposed to different types of cyber threats. A cybersecurity breach in an autonomous vehicle can cause serious problems, including a loss of public trust and safety. Therefore, it is very important to develop and apply strong cybersecurity measures to support the growth and acceptance of self-driving cars. This paper discusses major cybersecurity challenges like vulnerabilities in software and hardware, risks from wireless communication, and threats through external interfaces. It also reviews existing solutions such as secure software development, intrusion detection systems, cryptographic protocols, and anomaly detection methods. Additionally, the paper highlights the role of regulatory bodies, industry collaborations, and cybersecurity standards in creating a secure environment for autonomous vehicles. Setting clear rules and best practices is necessary for consistent protection across manufacturers and regions. By analyzing the current cybersecurity landscape and suggesting practical countermeasures, this paper aims to contribute to the safe development and public trust of autonomous vehicle technology.
Communication in Agile Software Development -- A Mapping Study
Venue: arXiv cs.SE 2025
Year: 2025

Software industry is a fast-moving industry and to keep up with this pace the development process also needs to be fast and efficient and Agile software development (ASD) is the answer to this problem. Even though ASD has been in there for over two decades there are still multiple unknown questions tied to ASD that need to be addressed. In this study we are going to address one of the most critical factors of ASD i.e. Communication. We conducted a review of 14 studies and found the areas under ASD communication that the community is interested in as well as research gaps.
Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies
Venue: arXiv cs.CV 2025
Year: 2025

Floods cause serious problems around the world. Responding quickly and effectively requires accurate and timely information about the affected areas. The effective use of Remote Sensing images for accurate flood detection requires specific detection methods. Typically, Deep Neural Networks are employed, which are trained on specific datasets. For the purpose of river flood detection in RGB imagery, we use the BlessemFlood21 dataset. We here explore the use of different augmentation strategies, ranging from basic approaches to more complex techniques, including optical distortion. By identifying effective strategies, we aim to refine the training process of state-of-the-art Deep Learning segmentation networks.
Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters
Venue: arXiv cs.CV 2025
Year: 2025

Drone-based crowd monitoring is the key technology for applications in surveillance, public safety, and event management. However, maintaining tracking continuity and consistency remains a significant challenge. Traditional detection-assignment tracking methods struggle with false positives, false negatives, and frequent identity switches, leading to degraded counting accuracy and making in-depth analysis impossible. This paper introduces a point-oriented online tracking algorithm that improves trajectory continuity and counting reliability in drone-based crowd monitoring. Our method builds on the Simple Online and Real-time Tracking (SORT) framework, replacing the original bounding-box assignment with a point-distance metric. The algorithm is enhanced with three cost-effective techniques: camera motion compensation, altitude-aware assignment, and classification-based trajectory validation. Further, Deep Discriminative Correlation Filters (DDCF) that re-use spatial feature maps from localisation algorithms for increased computational efficiency through neural network resource sharing are integrated to refine object tracking by reducing noise and handling missed detections. The proposed method is evaluated on the DroneCrowd and newly shared UP-COUNT-TRACK datasets, demonstrating substantial improvements in tracking metrics, reducing counting errors to 23% and 15%, respectively. The results also indicate a significant reduction of identity switches while maintaining high tracking accuracy, outperforming baseline online trackers and even an offline greedy optimisation method.
An Empirical Analysis of Compatibility Issues for Industrial Mobile Games
Venue: arXiv cs.SE 2025
Year: 2025

Detecting and fixing compatibility issues is critical for mobile game development. The rapid evolution of mobile operating systems and device fragmentation make it challenging for developers to timely address these issues across diverse models. Undetected compatibility problems can severely impact user experience and cause financial loss to companies and players. However, mobile game testing remains highly challenging, and compatibility issues are largely underexplored by the research community. To bridge this gap, we conduct an empirical study on common compatibility issues in commercial mobile games. We select four active and representative games with well-documented bug reports, totaling over seven million lines of code and over 20,000 commits. We build a comprehensive dataset linking bugs and fixes, enabling investigation into prevalent symptoms, root causes, and fixing strategies. Through extensive manual analysis, we categorize the most common symptoms and root causes, and summarize the typical fixes for each category. Our findings provide practical guidance for developers and offer insights to inspire future research on testing and fixing compatibility issues in mobile games.
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems
Venue: arXiv cs.CR 2025
Year: 2025

Digital twins (DTs) help improve real-time monitoring and decision-making in water distribution systems. However, their connectivity makes them easy targets for cyberattacks such as scanning, denial-of-service (DoS), and unauthorized access. Small and medium-sized enterprises (SMEs) that manage these systems often do not have enough budget or staff to build strong cybersecurity teams. To solve this problem, we present a Virtual Cybersecurity Department (VCD), an affordable and automated framework designed for SMEs. The VCD uses open-source tools like Zabbix for real-time monitoring, Suricata for network intrusion detection, Fail2Ban to block repeated login attempts, and simple firewall settings. To improve threat detection, we also add a machine-learning-based IDS trained on the OD-IDS2022 dataset using an improved ensemble model. This model detects cyber threats such as brute-force attacks, remote code execution (RCE), and network flooding, with 92\% accuracy and fewer false alarms. Our solution gives SMEs a practical and efficient way to secure water systems using low-cost and easy-to-manage tools.
Investigating task-specific prompts and sparse autoencoders for activation monitoring
Venue: arXiv cs.LG 2025
Year: 2025

Language models can behave in unexpected and unsafe ways, and so it is valuable to monitor their outputs. Internal activations of language models encode additional information that could be useful for this. The baseline approach for activation monitoring is some variation of linear probing on a particular layer: starting from a labeled dataset, train a logistic regression classifier on that layer's activations. Recent work has proposed several approaches which may improve on naive linear probing, by leveraging additional computation. One class of techniques, which we call "prompted probing," leverages test time computation to improve monitoring by (1) prompting the model with a description of the monitoring task, and (2) applying a learned linear probe to resulting activations. Another class of techniques uses computation at train time: training sparse autoencoders offline to identify an interpretable basis for the activations, and e.g. max-pooling activations across tokens using that basis before applying a linear probe. However, one can also prompt the model with a description of the monitoring task and use its output directly. We develop and test novel refinements of these methods and compare them against each other. We find asking the model zero-shot is a reasonable baseline when inference-time compute is not limited; however, activation probing methods can substantially outperform this baseline given sufficient training data. Specifically, we recommend prompted probing when inference-time compute is available, due to its superior data efficiency and good generalization performance. Alternatively, if inference-time compute is limited, we find SAE-based probing methods outperform raw activation probing.
Enhancing Systematic Reviews with Large Language Models: Using GPT-4 and Kimi
Venue: arXiv cs.CL 2025
Year: 2025

This research delved into GPT-4 and Kimi, two Large Language Models (LLMs), for systematic reviews. We evaluated their performance by comparing LLM-generated codes with human-generated codes from a peer-reviewed systematic review on assessment. Our findings suggested that the performance of LLMs fluctuates by data volume and question complexity for systematic reviews.
Generative Diffusion Models for Resource Allocation in Wireless Networks
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes a supervised training algorithm for learning stochastic resource allocation policies with generative diffusion models (GDMs). We formulate the allocation problem as the maximization of an ergodic utility function subject to ergodic Quality of Service (QoS) constraints. Given samples from a stochastic expert policy that yields a near-optimal solution to the constrained optimization problem, we train a GDM policy to imitate the expert and generate new samples from the optimal distribution. We achieve near-optimal performance through the sequential execution of the generated samples. To enable generalization to a family of network configurations, we parameterize the backward diffusion process with a graph neural network (GNN) architecture. We present numerical results in a case study of power control.
Deep Generative Prior for First Order Inverse Optimization
Venue: arXiv cs.AI 2025
Year: 2025

Inverse design optimization aims to infer system parameters from observed solutions, posing critical challenges across domains such as semiconductor manufacturing, structural engineering, materials science, and fluid dynamics. The lack of explicit mathematical representations in many systems complicates this process and makes the first order optimization impossible. Mainstream approaches, including generative AI and Bayesian optimization, address these challenges but have limitations. Generative AI is computationally expensive, while Bayesian optimization, relying on surrogate models, suffers from scalability, sensitivity to priors, and noise issues, often leading to suboptimal solutions. This paper introduces Deep Physics Prior (DPP), a novel method enabling first-order gradient-based inverse optimization with surrogate machine learning models. By leveraging pretrained auxiliary Neural Operators, DPP enforces prior distribution constraints to ensure robust and meaningful solutions. This approach is particularly effective when prior data and observation distributions are unknown.
Image Interpolation with Score-based Riemannian Metrics of Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models excel in content generation by implicitly learning the data manifold, yet they lack a practical method to leverage this manifold - unlike other deep generative models equipped with latent spaces. This paper introduces a novel framework that treats the data space of pre-trained diffusion models as a Riemannian manifold, with a metric derived from the score function. Experiments with MNIST and Stable Diffusion show that this geometry-aware approach yields image interpolations that are more realistic, less noisy, and more faithful to prompts than existing methods, demonstrating its potential for improved content generation and editing.
Radius-Guided Post-Clustering for Shape-Aware, Scalable Refinement of k-Means Results
Venue: arXiv cs.LG 2025
Year: 2025

Traditional k-means clustering underperforms on non-convex shapes and requires the number of clusters k to be specified in advance. We propose a simple geometric enhancement: after standard k-means, each cluster center is assigned a radius (the distance to its farthest assigned point), and clusters whose radii overlap are merged. This post-processing step loosens the requirement for exact k: as long as k is overestimated (but not excessively), the method can often reconstruct non-convex shapes through meaningful merges. We also show that this approach supports recursive partitioning: clustering can be performed independently on tiled regions of the feature space, then globally merged, making the method scalable and suitable for distributed systems. Implemented as a lightweight post-processing step atop scikit-learn's k-means, the algorithm performs well on benchmark datasets, achieving high accuracy with minimal additional computation.
DeepAndes: A Self-Supervised Vision Foundation Model for Multi-Spectral Remote Sensing Imagery of the Andes
Venue: arXiv cs.CV 2025
Year: 2025

By mapping sites at large scales using remotely sensed data, archaeologists can generate unique insights into long-term demographic trends, inter-regional social networks, and past adaptations to climate change. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. While recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multi-spectral satellite imagery, such as the 8-band data used in our study. In this paper, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multi-spectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for 8-band multi-spectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pre-trained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pre-training in archaeological remote sensing. Codes will be available on https://github.com/geopacha/DeepAndes.
Online Safety for All: Sociocultural Insights from a Systematic Review of Youth Online Safety in the Global South
Venue: arXiv cs.HC 2025
Year: 2025

Youth online safety research in HCI has historically centered on perspectives from the Global North, often overlooking the unique particularities and cultural contexts of regions in the Global South. This paper presents a systematic review of 66 youth online safety studies published between 2014 and 2024, specifically focusing on regions in the Global South. Our findings reveal a concentrated research focus in Asian countries and predominance of quantitative methods. We also found limited research on marginalized youth populations and a primary focus on risks related to cyberbullying. Our analysis underscores the critical role of cultural factors in shaping online safety, highlighting the need for educational approaches that integrate social dynamics and awareness. We propose methodological recommendations and a future research agenda that encourages the adoption of situated, culturally sensitive methodologies and youth-centered approaches to researching youth online safety regions in the Global South. This paper advocates for greater inclusivity in youth online safety research, emphasizing the importance of addressing varied sociocultural contexts to better understand and meet the online safety needs of youth in the Global South.
Perturbation-efficient Zeroth-order Optimization for Hardware-friendly On-device Training
Venue: arXiv cs.LG 2025
Year: 2025

Zeroth-order (ZO) optimization is an emerging deep neural network (DNN) training paradigm that offers computational simplicity and memory savings. However, this seemingly promising approach faces a significant and long-ignored challenge. ZO requires generating a substantial number of Gaussian random numbers, which poses significant difficulties and even makes it infeasible for hardware platforms, such as FPGAs and ASICs. In this paper, we identify this critical issue, which arises from the mismatch between algorithm and hardware designers. To address this issue, we proposed PeZO, a perturbation-efficient ZO framework. Specifically, we design random number reuse strategies to significantly reduce the demand for random number generation and introduce a hardware-friendly adaptive scaling method to replace the costly Gaussian distribution with a uniform distribution. Our experiments show that PeZO reduces the required LUTs and FFs for random number generation by 48.6\% and 12.7\%, and saves at maximum 86\% power consumption, all without compromising training performance, making ZO optimization feasible for on-device training. To the best of our knowledge, we are the first to explore the potential of on-device ZO optimization, providing valuable insights for future research.
Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approach
Venue: arXiv cs.LG 2025
Year: 2025

Bayesian experimental design (BED) offers a principled framework for optimizing data acquisition by leveraging probabilistic inference. However, practical implementations of BED are often compromised by model discrepancy, i.e., the mismatch between predictive models and true physical systems, which can potentially lead to biased parameter estimates. While data-driven approaches have been recently explored to characterize the model discrepancy, the resulting high-dimensional parameter space poses severe challenges for both Bayesian updating and design optimization. In this work, we propose a hybrid BED framework enabled by auto-differentiable ensemble Kalman inversion (AD-EKI) that addresses these challenges by providing a computationally efficient, gradient-free alternative to estimate the information gain for high-dimensional network parameters. The AD-EKI allows a differentiable evaluation of the utility function in BED and thus facilitates the use of standard gradient-based methods for design optimization. In the proposed hybrid framework, we iteratively optimize experimental designs, decoupling the inference of low-dimensional physical parameters handled by standard BED methods, from the high-dimensional model discrepancy handled by AD-EKI. The identified optimal designs for the model discrepancy enable us to systematically collect informative data for its calibration. The performance of the proposed method is studied by a classical convection-diffusion BED example, and the hybrid framework enabled by AD-EKI efficiently identifies informative data to calibrate the model discrepancy and robustly infers the unknown physical parameters in the modeled system. Besides addressing the challenges of BED with model discrepancy, AD-EKI also potentially fosters efficient and scalable frameworks in many other areas with bilevel optimization, such as meta-learning and structure optimization.
"I've talked to ChatGPT about my issues last night.": Examining Mental Health Conversations with Large Language Models through Reddit Analysis
Venue: arXiv cs.HC 2025
Year: 2025

We investigate the role of large language models (LLMs) in supporting mental health by analyzing Reddit posts and comments about mental health conversations with ChatGPT. Our findings reveal that users value ChatGPT as a safe, non-judgmental space, often favoring it over human support due to its accessibility, availability, and knowledgeable responses. ChatGPT provides a range of support, including actionable advice, emotional support, and validation, while helping users better understand their mental states. Additionally, we found that ChatGPT offers innovative support for individuals facing mental health challenges, such as assistance in navigating difficult conversations, preparing for therapy sessions, and exploring therapeutic interventions. However, users also voiced potential risks, including the spread of incorrect health advice, ChatGPT's overly validating nature, and privacy concerns. We discuss the implications of LLMs as tools for mental health support in both everyday health and clinical therapy settings and suggest strategies to mitigate risks in LLM-powered interactions.
What Causes Knowledge Loss in Multilingual Language Models?
Venue: arXiv cs.CL 2025
Year: 2025

Cross-lingual transfer in natural language processing (NLP) models enhances multilingual performance by leveraging shared linguistic knowledge. However, traditional methods that process all data simultaneously often fail to mimic real-world scenarios, leading to challenges like catastrophic forgetting, where fine-tuning on new tasks degrades performance on previously learned ones. Our study explores this issue in multilingual contexts, focusing on linguistic differences affecting representational learning rather than just model parameters. We experiment with 52 languages using LoRA adapters of varying ranks to evaluate non-shared, partially shared, and fully shared parameters. Our aim is to see if parameter sharing through adapters can mitigate forgetting while preserving prior knowledge. We find that languages using non-Latin scripts are more susceptible to catastrophic forgetting, whereas those written in Latin script facilitate more effective cross-lingual transfer.